1. Field of the Invention
The present invention relates to a plant operation support system employing a neural network, and more particularly to a plant operation support system which has high support capability such as displaying quantitative guidance as well as presenting the rational for the guidance.
The present invention also relates to a plant operation support system with a high support capability which displays only necessary data to an operator by periodically evaluating and selecting those items, having a large degree of influence upon operational control variables and operational state evaluation variables of a plant, from among a number of plant data items.
2. Description of the Prior Art
In operation management for water purification plants, sewage treatment plants, chemical reaction plants and so on, it is important to measure the number of data items of the plant through online processing, evaluate the plant state changing from time to time, and determine appropriate amounts of control variables.
As disclosed in Japanese Patent Laid-Open No. 63-240601 and No. 61-59502, for example, the approach of knowledge engineering for extracting heuristics of an operator and employing an inference engine along with the extracted knowledge is applied to the operation management.
A support system based on the knowledge engineering is persuasive to the operator because the inference process is logically clear and the reasons for the guidance derived from inference can be presented. On the contrary, it is troublesome in points of acquiring the experiential knowledge and carrying out maintenance service.
One solution of such a problem is disclosed as a support system based on a neural network model in Japanese Patent Laid-Open No. 1-224804.
With that model, the knowledge corresponding to experience and perception of the operator, which are included in operational historical data, is learned and only quantitative guidance is presented by association made in the model after the learning. If there is operational historical data, operation support can be realized by the association without the intricate process of acquiring the knowledge.
A conventional support system based on the neural network model has the following problems.
Since the learning and association using the neural network model are executed as a black box via only numerical data such as that given by the operational historical data and a group of parameters in the model, the knowledge learned in the model and the reasons of association are not seen by the operator. Therefore, the quantitative guidance by association is not sufficiently persuasive.
In an error backpropagation method for a multilayered neural network model, it sometimes happens that a parameter group (matrix of weighting factors) representing intensities of internal connections of the model is converged to a value which conflicts with knowledge (causality between input and output) contained in the operational historical data or knowledge which is not important for association. This may result in that the knowledge for the reasons of association is not correct and the association is not performed with a sufficient degree of accuracy.
Further, a large amount of online data derived from water purification plants, sewage treatment plants, chemical reaction plants and so on makes it difficult for the operator to supervise all those data at a time and judge the plant state. It is therefore important that only the control variables and those factors which greatly affect the state are selectively indicated to the operator from time to time.
One solution of addressing such a problem is proposed in Japanese Patent Laid-Open No. 64-88713, for example, as a method which comprises the knowledge engineering. This method describes the relationships between control variables or state evaluation variables and each datum in the form of rules based on heuristics for the operator, and determines those relationships through statistic calculations, thereby selectively displaying only the strong relationships.
That selective display serves as operation support for the operator to evaluate the plant state and determine appropriate amounts of the control variables.
Regarding the to selection of data, Japanese Patent Laid-Open No. 61-223944 discloses a technique for taking out from the data base in a memory only those data which satisfy the conditions specified. This technique includes plural condition registers for separately storing therein setting conditions, plural comparators for separately taking in the setting conditions from the condition registers, commonly taking in data from a memory which stores therein data inclusive of object data to be selected, and determining those ones of the taken-in data which satisfy the above setting conditions, and means for taking in outputs of the comparators, calculating the logical OR or AND of those outputs and taking out corresponding data from the memory dependent on the calculated result.
With the above technique, only those data satisfying the desired conditions can be selectively extracted.
The foregoing prior art however includes the following problem.
The method of selecting data with the aid of the knowledge engineering requires forming rules, as references for selection, based on the experiences of the operator and thus entails intricate acquisition of knowledge.
Further, when not only selecting the data but also determining amounts of the control variables and/or state evaluation variables of the plant with the knowledge engineering technique, other inferences than those for the data selection must also be executed, which renders the process intricate. In addition, logic for the data selection and logic for determining amounts of the control variables and/or state evaluation variables cannot be handled uniformly.
With logical operation technique, data of a data string within the same item which satisfy the conditions specified only are selected, and functions for evaluating whether or not selection of those data is necessary for the operator are not provided. Neither are there provided functions to determine the amount of control variables and/or state evaluation variables of the plant in a combined manner.